The era of “shared-nothing” architecture is reaching its physical limits in the modern cloud. As enterprise applications demand infinite transactional scaling and massive vector datasets for AI, Standard PostgreSQL’s coupled storage model creates a critical bottleneck known as write amplification. In this technical deep dive, we analyze the architectural paradigm shift introduced by Azure HorizonDB.

By decoupling compute from storage and utilizing a proprietary log-structured engine, HorizonDB promises to redefine write throughput and recovery times. We examine the internal mechanics of the “Log Service,” compare the cost-efficiency of DiskANN versus HNSW for RAG workloads, and visualize the asynchronous data flow to help cloud architects determine the definitive database engine for the AI era.

Updated October 2025

The Storage
Split.

Azure HorizonDB vs. Standard PostgreSQL. We analyze the shift from shared-nothing to shared-storage architectures in the cloud era.

IGT 2025 // REDMOND, WA

A technical breakdown of Microsoft’s new database engine.

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The Physical Limit

Standard PostgreSQL operates on a “coupled” model. The CPU and the storage live in the same virtual box. This creates friction. Every write requires a commit to the Write-Ahead Log (WAL) and a flush to the local disk.

HorizonDB decouples these layers. The compute nodes are stateless. The database log is the database. It pushes log records to a massive, distributed storage fleet that scales independently of the query engine.

The Key Difference

HorizonDB removes “Write Amplification.” It does not need to write data to a primary disk, then copy it to a replica disk. It writes once to shared storage.

Throughput Analysis

Under the Hood: The Log Service

HorizonDB introduces intermediate layers that Standard Postgres lacks. This complexity is what enables instant scaling.

1. The Compute Node

ROLE: QUERY PROCESSING

In this architecture, the “Postgres” instance is ephemeral. It contains no data. It caches pages in a local buffer pool (RBP) but relies on the network for truth. If this node crashes, a new one spins up and attaches to the storage in seconds.

2. The Page Server

ROLE: DATA MATERIALIZATION

Log records are just instructions (e.g., “change value A to B”). You cannot query logs directly. Page Servers constantly replay these logs in the background to generate updated 8KB data pages, which are then served back to the compute node upon request.

3. The Storage Layer

ROLE: DURABILITY

Built on Azure Premium Storage. This layer is responsible for the “L” in WAL. Once a log record hits this layer, the transaction is committed. It allows for Point-in-Time Restore (PITR) without performance penalties on the primary node.

Crash Recovery Mechanics

In standard Postgres, crash recovery can take minutes. The database must replay the WAL from the last checkpoint to bring the system to a consistent state.

HorizonDB eliminates this wait.

Since the log is separated, the storage layer (Page Servers) is always applying records in parallel. When the compute node restarts, it does not need to replay history. It simply connects to the storage and resumes serving queries.

Read Replica Lag

Standard replicas often drift seconds or minutes behind the primary during heavy write loads.

• Zero-Copy Replicas HorizonDB replicas read from the same shared storage. They do not maintain their own copy of the data.
• Milliseconds Lag Replicas only need to receive the latest log sequence number (LSN) to know what data is valid.

Visualizing the Flow

This visualization demonstrates the decoupled write path. Note how the “Compute” node sends logs to the “Log Service,” which then asynchronously updates the “Page Servers.”

• Active Compute
• Infrastructure
• Data Stream

Architecture Map

Specs vs Specs

Feature	Standard PostgreSQL	Azure HorizonDB
Storage Model	Coupled (Local SSD)	Decoupled (Shared Pool)
Max Capacity	~32 TB	128 TB+
Scaling Speed	Minutes (Data Copy)	Seconds (Metadata Only)
Vector Index	HNSW (RAM Heavy)	DiskANN (SSD Optimized)
Write Latency	Disk I/O Bound	Network Log Bound (Faster)

HNSW Limitation

Standard `pgvector` uses Hierarchical Navigable Small Worlds. This algorithm is fast but requires the index to reside in RAM. For 100M vectors, this demands expensive high-memory VMs.

DiskANN Advantage

HorizonDB uses DiskANN. It stores the bulk of the vector graph on SSDs, keeping only a lightweight map in RAM. This reduces infrastructure costs by approximately 85% for large datasets.

Cost Efficiency

Recommendation Engine

Early Stage Enterprise Core AI / RAG Low Budget

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